CVbinary {DAAG}R Documentation

Cross-Validation for Regression with a Binary Response

Description

This function gives training (internal) and cross-validation measures of predictive accuracy for regression with a binary response. The data are randomly divided between a number of `folds'. Each fold is removed, in turn, while the remaining data are used to re-fit the regression model and to predict at the omitted observations.

Usage

CVbinary(obj=frogs.glm, rand=NULL, nfolds=10, print.details=TRUE)

cv.binary(obj=frogs.glm, rand=NULL, nfolds=10, print.details=TRUE)

Arguments

obj a glm object
rand a vector which assigns each observation to a fold
nfolds the number of folds
print.details logical variable (TRUE = print detailed output, the default)

Value

cvhat predicted values from cross-validation
internal internal or (better) training predicted values
training training predicted values
acc.cv cross-validation estimate of accuracy
acc.internal internal or (better) training estimate of accuracy
acc.training training estimate of accuracy

Note

The term ‘training’ seems preferable to the term ‘internal’ in connection with predicted values, and the accuracy measure, that are based on the observations used to derive the model.

Author(s)

J.H. Maindonald

See Also

glm

Examples

frogs.glm <- glm(pres.abs ~ log(distance) + log(NoOfPools), 
                 family=binomial,data=frogs)
CVbinary(frogs.glm)
mifem.glm <- glm(outcome ~ ., family=binomial, data=mifem)
CVbinary(mifem.glm)

[Package DAAG version 0.99-3 Index]